Zhou, Jiaji and Ross, Stephane and Yue, Yisong and Dey, Debadeepta and Bagnell, J. Andrew (2013) Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization. In: Inferning: Interactions between Inference and Learning (WINFERN), 20 June 2013, Atlanta, GA. (Submitted) https://resolver.caltech.edu/CaltechAUTHORS:20190327-085828098
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Abstract
We study the problem of predicting a set or list of options under knapsack constraint. The quality of such lists are evaluated by a submodular reward function that measures both quality and diversity. Similar to DAgger (Ross et al., 2010), by a reduction to online learning, we show how to adapt two sequence prediction models to imitate greedy maximization under knapsack constraint problems: CONSEQOPT (Dey et al., 2012) and SCP (Ross et al., 2013). Experiments on extractive multi-document summarization show that our approach outperforms existing state-of-the-art methods.
| Item Type: | Conference or Workshop Item (Poster) | |||||||||
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| Additional Information: | © 2013 by the author(s). Presented at the International Conference on Machine Learning (ICML) workshop on Inferning: Interactions between Inference and Learning, Atlanta, Georgia, USA, 2013. This research was supported by NSF NRI Purposeful Prediction and the Intel Science and Technology Center on Embedded Computing. We gratefully thank Martial Hebert for valuable discussions and Alex Kulesza for providing data and code. | |||||||||
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| Record Number: | CaltechAUTHORS:20190327-085828098 | |||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20190327-085828098 | |||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
| ID Code: | 94185 | |||||||||
| Collection: | CaltechAUTHORS | |||||||||
| Deposited By: | George Porter | |||||||||
| Deposited On: | 27 Mar 2019 23:15 | |||||||||
| Last Modified: | 03 Oct 2019 21:01 |
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